Cost-effectiveness of point-of-care versus centralised, laboratory-based nucleic acid testing for diagnosis of HIV in infants: a systematic review of modelling studies

Summary Background Point-of-care (POC) nucleic acid testing for diagnosis of HIV in infants facilitates earlier initiation of antiretroviral therapy (ART) than with centralised (standard-of-care, SOC) testing, but can be more expensive. We evaluated cost-effectiveness data from mathematical models comparing POC with SOC to provide global policy guidance. Methods In this systematic review of modelling studies, we searched PubMed, MEDLINE, Embase, the National Health Service Economic Evaluation Database, Econlit, and conference abstracts, combining terms for “HIV” + “infant”/”early infant diagnosis” + “point-of-care” + “cost-effectiveness” + “mathematical models”, without restrictions from database inception to July 15, 2022. We selected reports of mathematical cost-effectiveness models comparing POC with SOC for HIV diagnosis in infants younger than 18 months. Titles and abstracts were independently reviewed, with full-text review for qualifying articles. We extracted data on health and economic outcomes and incremental cost-effectiveness ratios (ICERs) for narrative synthesis. The primary outcomes of interest were ICERs (comparing POC with SOC) for ART initiation and survival of children living with HIV. Findings Our search identified 75 records through database search. 13 duplicates were excluded, leaving 62 non-duplicate articles. 57 records were excluded and five were reviewed in full text. One article was excluded as it was not a modelling study, and four qualifying studies were included in the review. These four reports were from two mathematical models from two independent modelling groups. Two reports used the Johns Hopkins model to compare POC with SOC for repeat early infant diagnosis testing in the first 6 months in sub-Saharan Africa (first report, simulation of 25 000 children) and Zambia (second report, simulation of 7500 children). In the base scenario, POC versus SOC increased probability of ART initiation within 60 days of testing from 19% to 82% (ICER per additional ART initiation range US$430–1097; 9-month cost horizon) in the first report; and from 28% to 81% in the second ($23–1609, 5-year cost horizon). Two reports compared POC with SOC for testing at 6 weeks in Zimbabwe using the Cost-Effectiveness of Preventing AIDS Complications-Paediatric model (simulation of 30 million children; lifetime horizon). POC increased life expectancy and was considered cost-effective compared with SOC (ICER $711–850 per year of life saved in HIV-exposed children). Results were robust throughout sensitivity and scenario analyses. In most scenarios, platform cost-sharing (co-use with other programmes) resulted in POC being cost-saving compared with SOC. Interpretation Four reports from two different models suggest that POC is a cost-effective and potentially cost-saving strategy for upscaling of early infant testing compared with SOC. Funding Bill & Melinda Gates Foundation, Unitaid, National Institute of Allergy and Infectious Diseases, National Institute of Child Health and Human Development, WHO, and Massachusetts General Hospital Research Scholars


Introduction
Early initiation of antiretroviral therapy (ART) substantially improves the survival and health of children living with HIV. 1 Without ART, a third of children living with HIV do not survive the first year of life. 2 ART for symptomatic infants reduces infant mortality, but benefits vary by timing of initiation. 3 In 2007, a landmark trial compared same-day initiation to deferred initiation (based on clinical parameters) of treatment in asymptomatic infants (median age 7 weeks). 1 Within 9 months of follow-up, the early treatment group had a 76% survival benefit, despite 66% of infants in the deferred initiation group having initiated ART following clinical deterioration. A stark mortality difference was already detectable within the first 3 months; two in three deaths occurred within 6 months of follow-up, demonstrating the relative urgency of ART in infants. 1 Accordingly, WHO advocates universal ART for children living with HIV, with a strong recommendation for rapid initiation. 4 However, early treatment initiation requires early diagnosis, which in turn requires testing access with prompt turnaround times for, and clinically appropriate management responses to, positive results. As HIV infection in children younger than 18 months (hereafter referred to as infants) can only be reliably diagnosed using molecular testing (nucleic acid testing, NAT), implementation strategies to achieve early diagnosis have been hampered by logistical and cost constraints in most high burden settings. 5,6 In 2020, a third of infants born to women living with HIV received no HIV testing within the recommended first 2 months of life globally, while 75% did not receive timely testing in west and central Africa. 7 In addition, 52% of all children younger than 15 years living with HIV remained undiagnosed. 7 However, new technological advances have enabled pointof-care (POC) NAT-based testing, allowing decentralised diagnosis by trained non-laboratory staff and facilitating same-day results with rapid ART initiation for children living with HIV. 4,5,8 POC testing has the potential to revolutionise infant diagnosis from logistic and cost perspectives, enabling rapid scale-up for early testing and ART initiation. A strong evidence base supports accuracy, feasibility, and acceptability of POC infant diagnosis. [9][10][11][12] Mathematical modelling studies enable broader understanding and evaluation of the cost-effectiveness and implications of health-care interventions, under different clinical and contextual scenarios and using a range of anticipated settings and costs. 13 However, modelling studies have inherent limitations, predominantly related to quality of the data used in the model, model structure, and implicit assumptions. 14 Synthesis of findings from different cost-effectiveness models can boost understanding of the broader applicability of findings, to provide guidance for optimal resource allocation in the context of paediatric HIV. 15 We aimed to systematically assess reports of costeffectiveness models comparing POC-NAT with centralised, laboratory-based NAT (standard of care, SOC) for infant HIV diagnosis (<18 months).

Search strategy and selection criteria
Following a previously developed protocol (appendix pp 1-4) we used iterative search strategies in PubMed,

Research in context
Evidence before this study Point-of-care (POC) nucleic acid testing (NAT) for infant diagnosis of HIV has the potential to greatly improve progress to elimination of paediatric HIV and AIDS. Compared with the current standard of care (SOC; centralised laboratory-based NAT), POC-NAT for infant diagnosis showed high diagnostic accuracy and substantial clinical benefit in several systematic reviews and meta-analyses. However, with budgetary constraints, programmatic concerns around implementation of POC-NAT for infant diagnosis centre on the cost-effectiveness and affordability of POC compared with SOC. Mathematical modelling of cost-effectiveness enables contextualisation of both costs and effectiveness under various assumptions and different, simulated situations. We did a systematic literature review of published and unpublished studies reporting cost-effectiveness of POC compared with SOC-NAT for infant HIV diagnosis using mathematical models. We searched PubMed, MEDLINE, Embase, the NHS Economic Evaluation Database, Econlit, and conference abstracts, combining terms for "HIV" + "infant"/"early infant diagnosis" + "point-of-care" + "cost-effectiveness" + "mathematical models", without restrictions from database inception to July 15, 2022. We extracted data on health and economic outcomes from the identified original studies, and incremental cost-effectiveness ratios (ICERs) for synthesis. Study quality was assessed using the Consolidated Health Economic Evaluation Reporting Standards checklist. Model focus, approaches, and assumptions differed widely, resulting in a range of ICER estimates, with degree of cost-effectiveness varying by publication. To provide policy guidance regarding the cost-effectiveness of POC versus SOC NAT for infant HIV diagnosis, we provide a narrative synthesis of all cost-effectiveness modelling data currently available.

Added value of this study
The robustness of data from individual modelling studies is directly influenced by model structure, assumptions, and input data. Synthesis of data from different cost-effectiveness models can partly address some of these shortcomings, boosting understanding of the broader applicability of findings, and provide guidance for optimal resource allocation in the context of paediatric HIV. Our structured summary of model results, similarities, and differences provides policy makers with a single overview alongside additional insight that seeks to translate knowledge to action.

MEDLINE, Embase, National Health Service Economic
Evaluation Database (University of York Centre for Reviews and Dissemination), Econlit, and conference abstracts. We adjusted search strategies by database, combining terms for "HIV" + "infant"/"early infant diagnosis" + "point-of-care" + "cost-effectiveness" + "mathematical models", without res trictions (last full search Feb 26, 2020; updated search July 15, 2022; appendix p 5). Eligibility was assessed using a population, intervention, comparison, outcomes, and study (PICOS) framework (population [HIV-exposed children younger than 18 months], intervention [POC-NAT for primary diagnosis of HIV], comparator [laboratory-based NAT for primary diagnosis of HIV], outcome [cost-effectiveness], study design [mathematical modelling]). The primary outcomes of interest were incremental cost-effectiveness ratios (ICERs, comparing POC with SOC) for ART initiation and survival of children living with HIV. Two reviewers (SMlR and JO) reviewed titles and abstracts independently, with full-text review for any article potentially addressing the PICOS question. Disagreements were resolved through discussion. Studies not addressing the full PICOS framework were excluded.

Quality assessment, data extraction, and analysis
We assessed methodological quality using the Consolidated Health Economic Evaluation Reporting Standards statement (appendix pp 6-11). 16 We extracted data on geographical setting, model type, population, perspective, time horizon, and discounting; model assumptions and parameters, and scenario and sensitivity analyses; platforms and testing algorithms; overall health outcomes and economic outcomes; and relative costeffectiveness, measured in ICERs. All costs were extracted as per the publication, and, where relevant, adjusted to 2018 US$ using the Consumer Price Index Inflation calculator. ICERs were reported as per the index publication. The Preferred Reporting Items for Systematic Reviews and Meta-analyses checklist for this research is included in the appendix (pp [12][13]. Data were synthesised qualitatively, in recognition of the substantial heterogeneity of the available mathematical models.

Role of the funding source
The funders had no role in study design or conduct; the collection, analysis, or interpretation of the data; or the preparation, review, or approval of the manuscript. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.

Results
Our search identified 75 records including 13 duplicates. 57 of 62 non-duplicate articles were excluded and five were reviewed in full text. One article was excluded as it was not a modelling study, and four qualifying studies were included in the review (figure, appendix p 14). [17][18][19][20][21] These four reports were from two mathematical models from two independent modelling groups (table 1). The Cost-Effectiveness of Preventing AIDS Complications-Paediatric (CEPAC-P) model was used to evaluate clinical benefits and cost-effectiveness of POC versus SOC at 6 weeks of age 18 and cost-effectiveness of POC versus strengthened laboratory-based testing (S-SOC) versus SOC, at age 6 weeks; 19 both reports focused on Zimbabwe. The Johns Hopkins University (JHU) model assessed cost-effectiveness of POC versus SOC for a representative setting in sub-Saharan Africa, with testing at age 6 weeks and 9 months; 20 and cost-effectiveness of POC versus SOC in Zambia, with testing at birth and age 6 weeks and 6 months. 21 All reports were considered high quality. Model parameters are shown in table 2, with additional details in the appendix (pp . There were notable similarities and differences between model assumptions and outcomes. All four reports estimated ICERs comparing POC with SOC for scale-up of testing services, in high HIV burden settings. All models assumed pre-existing SOC infant diagnosis testing systems, with peripheral clinics sending batches of dried blood spots to a centralised laboratory. Cost parameters, based on programmatic and research data, included capital and recurrent costs for scale-up and maintenance (data sources are shown in the appendix pp 15-36). 17,[22][23][24][25][26][27] All models assumed an integrated platform use in SOC, with costs and use shared between early infant diagnosis (EID), HIV viral load, and tuberculosis testing. For example, using data provided by the Clinton Health Access Initiative, JHU model 1 assumed 50% of SOC platforms were used for EID purposes. Across models, SOC results were returned using electronic communication tools where available, with variable delays and probability of initiating ART    Two reports (one from each model) provided budgetary impact analyses, both with favourable conclusions. 19,20 Differences between the reports primarily reflected differences in modelling approaches, outcome definitions, modelled age(s) of testing, and implementation approaches for the upscaling of POC testing (tables 1, 2). The CEPAC model is a state-transition model with a lifetime horizon for both costs and health outcomes. 18,19 Using monthly cycles, the model has been extensively validated and calibrated for clinical variability across risks for HIV transmission, HIV disease progression, acute and chronic morbidity, and mortality. 28 The model allows substantial variation in clinical input parameters and ART efficacy across time. Both of the CEPAC modelling analyses included in this manuscript were based on the POC implementation strategy and pilot programme assessment data from the Elizabeth Glaser Pediatric AIDS Foundation (EGPAF) and Unitaid EID initiative in Zimbabwe. 17 Both reports focused on US$ per year of life saved (YLS), with testing at age 6 weeks, comparing POC with SOC. [17][18][19] CEPAC model 2 also included a third comparison group of S-SOC, using data from a study in Kenya. 22 This strategy included improved sample transport (daily instead of weekly as per SOC), a data tracking system with electronic alerts, additional laboratory staff and training, and improved laboratory maintenance. 22 By comparison, the JHU model is a decision-tree model with shorter time horizons for both costs (9 months for the sub-Saharan African analysis and 5 years for the Zambianfocused analysis) and outcomes (age 18 months for sub-Saharan Africa and 12 months for Zambia). 20,21 The JHU analyses modelled a cohort of children over time, with various probabilities of leaving and re-entering care for repeat testing over time; input data were predominantly from previous EID projects. 26,27,[29][30][31] The primary outcome for both JHU models was the probability of ART initiation within 60 days in children living with HIV; additional outcomes were the probability of ever initiating ART (by 18 months in sub-Saharan Africa and 12 months in Zambia), and HIV-related deaths averted before ART initiation. The JHU modelling reports also assessed the effect on clinical outcomes and cost-effectiveness of different practical approaches to scaling up of POC, by comparing ICERs for POC versus SOC across different testing algorithms and implementation models. 20,21 The two modelling approaches aligned in overall conclusions, indicating cost-effectiveness of POC compared with SOC. In both JHU modelling analyses, a substantially greater proportion of children living with HIV was projected to initiate ART within 60 days of testing. However, as costs were spread over only 9 months compared with 5 years, the ICERs for model 1 were markedly higher than model 2. ART initiation probability increased from 19% to 82% (ICER per additional ART initiation, range $430-1097) in model 1; and from 28% to 81% in model 2 (ICER per additional ART initiation, SOC=standard-of-care (centralised laboratory testing with dried blood spots sent in from peripheral clinics). POC=point-of-care. NAT=nucleic acid testing. ICER=incremental cost-effectiveness ratio (comparing POC with SOC). ART=antiretroviral therapy. EID=early infant diagnosis. JHU=Johns Hopkins University. *POC with testing algorithm using POC for both primary and confirmatory test and for tie-breaker tests where required; implementation for JHU model 1 not specified, but for JHU model 2, implementation model based on 61% of clinics covered with POC on premises, and 39% peripheral (low turnover) testing clinics referred mother-infant pairs directly to clinics with on-site POC; testing costing for mPIMA not shown as more expensive with higher ICERS across outcomes; although GeneXpert Edge showed better ICERS than IV in JHU model 1, only GeneXpert-IV results shown here, to optimise model comparability. †GeneXpert used for EID, use 15% for JHU model 1 and 10% for JHU model 2 (costs shared by tuberculosis programme and non-EID HIV programmes, for viral load testing and diagnostic purposes); assumes that EID is prioritised over viral load and tuberculosis testing. ‡Decision-tree model with 9-month horizon for costing and 18 months for clinical outcomes, focused broadly on sub-Saharan Africa; assumes prevention of vertical transmission coverage range of 93%, 96% sensitivity of POC assay, and 20% input probability of ART initiation within 60 days. §Decision-tree model with 5-year horizon for costs and 12 month clinical outcomes, focused on Zambia; assumes prevention of vertical transmission coverage of 93%, 97% sensitivity of POC assay, and 30% input probability of ART initiation within 60 days.  were notably higher than those in the JHU models, reflecting lifetime HIV and ART treatment costs in CEPAC models, with effectiveness measured by potential clinical consequences of POC testing rather than shortterm probabilities of ART initiation. In CEPAC model 2, S-SOC was associated with only slightly better 1-year survival for children living with HIV than SOC (69·9% vs 67·3%; undiscounted life expectancy 22·71 vs 21·74 years), but worse survival than POC (75·6%; life expectancy 24·49 years). 19 Similarly, costs for S-SOC were lower than for POC but higher than for SOC. In cost-effectiveness analysis, S-SOC was weakly dominated (ie, a less efficient use of resources compared with both the other strategies, with higher ICER despite lower cost of resources). In budget impact analysis, the SOC testing approach linked 1680 children living with HIV to HIV care at a cost of $15·7 million (0·93% of the total national HIV budget of Zimbabwe in 2017). By contrast, the POC testing approach (GeneXpert-IV) linked 4480 children living with HIV to care at $23·1 million (1·37% of the total Zimbabwean HIV budget, appendix p 38). 19 All four models used extensive scenario and sensitivity analyses (appendix pp 15-36); cost-effectiveness curves are shown in each of the original publications. [18][19][20][21] Salient results from variation in key parameters are summarised here. Both JHU model reports assessed the impact of prevention of vertical transmission (ie, maternal ART) coverage. 20,21 For sub-Saharan Africa (JHU model 1), using POC with a 9-month cost horizon and keeping all other model parameters constant, a low prevention of vertical transmission coverage of 48% resulted in lower ICER than for coverage of 93% ($417 vs $966 per additional ART initiation within 60 days). 20 This result reflected a higher prevalence of infant HIV, conversely increasing the number of children living with HIV and initiating ART. In the Zambian report (JHU model 2), ICER did not vary substantially across coverage, ranging from 73% to 99%. 21 Both CEPAC models assessed the potential impact of reduced ART effectiveness following a POC (but not SOC) HIV diagnosis. In model 1 (base-case 82-91% ART efficacy following POC, depending on age), the POC versus SOC ICER remained similar within a plausible range of ART efficacy (70-100%). 19 POC only became less cost-effective than SOC in the unlikely scenario of post-POC testing ART efficacy below 45%. POC (vs SOC) also remained cost-effective when ART efficacy was reduced for both POC and SOC. ICERs remained robust through the full tested ART efficacy range in CEPAC model 2 (90-96%). 19 The CEPAC models also assessed the effects of varying treatment costs. In model 1, POC testing was no longer cost-effective compared with SOC if HIV-related healthcare costs doubled, or ART costs tripled, across both SOC and POC strategies. 18 ICERs remained below the threshold across the examined range of treatment costs (sensitivity analysis range, 0·5-fold to three-fold increase in costs). 19 Variations in SOC diagnostic accuracy, turnaround time, and probability of ART initiation were assessed in both CEPAC and JHU models. In the CEPAC model 1, POC remained more cost-effective than SOC across a range of plausible variations in SOC assay characteristics, turnaround time, and probability of initiating ART, in oneway, most multi-way, and seven of nine best-worst case scenario analyses (appendix pp . 18 In all scenario analyses, POC testing resulted in greater life expectancy for children living with HIV. In the JHU models, the relative cost-effectiveness of POC decreased as the programmatic performance of SOC improved. For example, in the JHU model 1 report, increasing the probability of ART initiation within 60 days following SOC testing from 25% to 75% reduced the projected number of additional children living with HIV initiating ART after POC from 880 to 150. At base-case costing levels, POC was no longer cost-effective if more than 60% of children living with HIV initiated ART within 60 days of SOC (vs basecase scenario, 15-27% initiations). 20 In the JHU model 2, ICERs remained stable when the probability of initiating ART within 60 days after SOC increased from 30% (base-case) to 43% (ICER changed from $23 to $22 per additional ART initiation). Across models, a lower POC sensitivity was associated with lower POC effectiveness and, in turn, higher ICERs. POC remained cost-effective compared with SOC within plausible POC assay sensitivity ranges in CEPAC model 1 (93·3-99%), only losing cost-effectiveness at the implausibly low sensitivity of 65%. 18 In the JHU models, POC was potentially cost-saving even when POC sensitivity was as low as 80% (appendix pp . 21 Different POC implementation strategies affected degree of cost-effectiveness substantially. JHU model 2 directly compared three POC implementation approaches against SOC in Zambia, assuming 100% use of the POC platform for infant HIV diagnosis. 21 In the base-case (implementation model 1), POC platforms were placed at 40 high turnover facilities (>1·5 tests per day), covering about 60% of all HIV-exposed infants with other sites referring mother-infant pairs directly to these facilities (assumes no testing delays, and no health system costs for referral). An expanded access implementation model (model 2) placed platforms at 74 facilities to cover 77% of HIV-exposed infants, with 23% of tests sent to centralised laboratories (SOC). The third implementation model, termed a hub-and-spoke model, used the same facilities and number of platforms as the first model (hubs), while about 40% of HIV-exposed children provided dried blood spots at the lower throughput sites (spokes) for transport to the hubs. Compared with the base-case model, ICERs for the expanded JHU implementation model were 2-66 times higher, and 1·2-6·6 times higher for the hub-and-spoke model, comparing POC with SOC. 21 By contrast, CEPAC model analyses were based only on a hub-and-spoke model, as implemented by the EGPAF and Unitaid in Zimbabwe, with two key differences from the Zambian JHU hub-and-spoke model. First, the CEPAC model assumed cost integration (POC platform use 60-90%) compared with the 100% use of the base-case JHU POC model. Second, in the Zimbabwean hub-andspoke model, spokes were within 1 h travel from hub sites, to enable transport of rapid dried blood spots. The turnaround time for spoke site test results was shorter for the Zimbabwean than the Zambian model (7 days compared with 4 weeks). 19,21 In all analyses, variations in cost per test (reflecting capital and recurrent costs) affected overall ICER estimates (appendix pp 15-36). [21][22] Longer time spent on POC testing (partly reflecting staff skill and training) increased the recurrent costs and, therefore, ICER in the same model (appendix pp . However, in CEPAC model 1, POC remained cost-effective compared with SOC across a plausible range of cost per test (base-case $27·61; range $10-50 in 2016 US$). 18 Shared use of platforms (both HIV viral load and tuberculosis diagnostics for GeneXpert and HIV viral load only for m-PIMA) was assumed for all SOC scenarios, as is standard for most centralised laboratories. Shared use of platforms in a POC setting substantially improved ICER estimates. Both JHU models evaluated two POC payment approaches: 100% of the capital and recurrent costs associated with POC paid by EID programmes (no shared use), versus POC platform sharing with other services (tuberculosis diagnostics and POC HIV viral load monitoring), with EID programmes paying pro rata by percentage of use. 20

Discussion
Across all available cost-effectiveness mathematical models, POC-NAT-based infant diagnosis of HIV increased treatment coverage and improved survival of children living with HIV compared with SOC, facilitated by earlier diagnosis and timely initiation of ART. [18][19][20][21] Without shared use of POC platforms, the POC testing strategy might require greater overall expenditure than SOC, but ICERs for all base-case scenarios and outcomes were within acceptable ranges across different clinical outcomes, geographical settings, and testing approaches. These data therefore strongly support appropriately implemented POC as a cost-effective strategy to improve the timely diagnosis and health of children living with HIV.
Moreover, the shorter time horizon models (JHU models 1 and 2) provided compelling evidence that POC could be cost-saving compared with SOC, given shared platform use and cost. 20,21 This is a key finding of the review, with substantial policy implications. In support of this, a costing study for POC-based infant diagnosis of HIV in Zimbabwe showed that the most significant drivers of cost relate to materials and supplies. 24 Nonetheless, ongoing collection and review of programmatic costing and effectiveness data should remain a priority as countries scale up their national infant HIV diagnostic programmes, with broader implementation of POC platforms.
Scaling up of POC infant HIV diagnostics might also be affordable in most high HIV burden settings. In the context of the Zimbabwean HIV care and prevention budget, moving from SOC to POC increased spending by an estimated 0·44% of the overall HIV budget and linked 167% more children living with HIV to care over 5 years (CEPAC model 2). 19 That is, for every 100 children living with HIV initiating ART following SOC-NAT, more than 260 children living with HIV could initiate ART following POC, with less than 1% increased spending in the overall budget. Encouragingly, similar budgetary impact results were noted in JHU model 1. 20 A broad range of sensitivity analyses across reports highlighted some important considerations around POC implementation. Cost-effectiveness was maximised with targeted implementation in settings with poor SOC performance and low prevention of vertical transmission coverage, provided that results have rapid turnaround times with prompt ART initiation and retention in care for optimal treatment effectiveness. Increasing capital and recurrent costs-as might result from reduced lifespan of POC instruments and increased test run times-will negatively affect cost-effectiveness, emphasising the importance of POC maintenance, staff training and support, and uninterrupted supplies.
These models share the known limitations of mathematical models, including the requisite simplification of complex processes with reliance on assumptions and data from multiple sources. 15 However, the structure and processes of infant testing were modelled explicitly and reported transparently, using data from high-quality sources for both costing and clinical outcomes. [18][19][20][21] Despite multiple differences, the model findings are congruent. Nonetheless, the absence of long-term clinical data following POC testing is an important limitation, and programmatic data are urgently needed to this end. [18][19][20][21] By design, costeffectiveness models from a health-care perspective do not reflect societal or patient perspectives on potential costs and gains. POC infant testing has high reported maternal acceptability, an important consideration for policy makers seeking patient-centred interventions. [33][34][35][36][37] Although we used an extensive search strategy, our review is limited to only four reports from two models, all focused on Africa; generalisability is a concern. Nonetheless, the highest incidence and burden of HIV is in sub-Saharan Africa, which is well represented in these models. 7 We focused on cost-effectiveness models and did not include costing analyses from clinical trials. 38 Although there are inherent limitations to mathematical models, this approach enables simultaneous comparison of different strategies and approaches beyond the timelines of available data. These cost-effectiveness analyses support the findings of published costing analyses. 17,24 Direct comparison of ICERs between models is complicated by differences in denomination of the primary outcomes ($ per diagnosis vs $ per YLS). There is no established cost-effectiveness threshold for the former outcome, and debate exists regarding the use of Gross Decimal Product for cost-effectiveness thresholds in general. 39,40 Nonetheless, where an intervention (here, POC testing as modelled in JHU model 2 with shared platform use) is both more effective and less expensive than the alternative (here, SOC testing), concerns around cost-effectiveness thresholds do not apply.
In conclusion, the use of POC-NAT for infant HIV diagnosis is a cost-effective-and potentially costsaving-strategy compared with laboratory-based NAT, with significant benefits for children living with HIV. However, considered implementation of POC testing platforms, with adequate support and maintenance strategies for both testing and treatment initiation, are required to minimise costs and optimise outcomes. In 2020, the estimated ART coverage for children younger than 15 years was only 54% globally, compared with 74% among adults. 41 Implementation of POC infant diagnosis is a first step towards addressing this equity gap in HIV treatment coverage, alongside strong antiretroviral treatment programmes for both mothers and children.

Contributors
SMlR and JO did the systematic literature search and screened all abstracts and relevant full-text articles. SMlR completed the narrative synthesis, generated figures for visualisation, and wrote the original draft. All authors reviewed the manuscript and provided editorial insight. CGS, PPS, GdB, and DD provided pre-publication data for consideration in the review. CGS, PPS, GdB, DD, ALC, NCM, and SCF were the primary or senior authors on the four included manuscripts and provided oversight of data interpretation and representation. LM and LV supervised the literature search and data abstraction. LV was responsible for conceptualisation of the review and project administration. All authors contributed to and approved the final manuscript. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication. CGS and ALC had access to and verified the data in the original studies. LV and SMlR had full access to and verified the data summaries presented in this manuscript.

Declaration of interests
We declare no competing interests.

Data sharing
All data used in this manuscript are freely available in the published reports included for the synthesis.